FIBO, FIBO, It's Off To A Financial Industry Business Ontology We Go

Credit default swaps. Collateralized debt obligations. Moral hazards. The average person might find the financial services sector and its language as mystifying as some of those involved in the industry might find semantic technology. An event hosted by OMG and the EDM Council in New York City yesterday was aimed at demystifying the latter for Wall Street. But putting the technology to work there might help clarify the discourse around financial instruments for a wider audience, including the regulators who want to deal with concentration of risk issues that played a big role in the Wall Street meltdown.

One part of the picture is FIBO, the Financial Industry Business Ontology, which was the subject of two sessions at the event. An advance discussion of the topic with Thematix principals Elisa Kendall and Jim Rhyne, who was a panelist at the event, set the stage for us here at The Semantic Web Blog. “The primary practical use for an ontology like FIBO that is descriptive of various kinds of financial instruments, including so-called exotics, is that regulators and financial market participants get a common language to talk about things,” Rhyne explains. This is important, given that financial regulators try hard to be collaborative with the industry, pointing out the need, he says, for careful management of financial instruments, including recommendations about capital buffers to deal with downside risk and asking for timely reports of information that would allow them to assess the possibility that a systemic problem could occur rather than directly intervening by stopping trades.

Especially in the derivatives marketplace, there is a lot of “funky terminology,” he says, and not all of it is as well-understood as it should be. Different parties and different parts of the marketplace may call the same instrument by different terms, and one of FIBO’s aims is to provide a common vocabulary.

Kendall adds that it may be helpful to think of something like credit default swaps as the financial industry’s version of designer drugs. “They get a new name every day and are repackaged” as soon as regulators identify an issue with them, she says. “With really decent ontologies to describe the parts of these contracts or swap agreements, you can use the reasoning to say that you may have renamed this, but it has all the same properties that this other thing had that we didn’t like. So you are taking a boatload of attributes describing the properties of these agreements and constraints on them, and all the parties to them, and using the reasoning to unravel it.”

In play as part of this is categorizing instruments according to their contractual components, to help understand, for example, whether some third party has accumulated a large position of risk. While every financial trade follows a standard contract template, ISDA-approved (International Swaps and Derivatives Association) contract components can be put together buffet-style, and there has been no taxonomy of financial instruments to improve comprehension of their details or to interpret risks or compliance factors from the XML-based messages that are used for exchanging information to reconcile trade positions.

ISDA, Rhyne says, is a good example of how an industry can regulate itself; the decision it made to try to standardize derivatives and swaps contracts has substantially leveled the playing field by eliminating “contract surprises” and making the contracts clearer. But it has no mandate to deal with systemic risk, or even portfolio or other kinds of risks that may be undertaken by its members, he says.

So, “besides a common vocabulary you need an ontology to categorize instruments according to the contract components they have,” says Rhyne. “This is where semantic technology excels,” for creating definitions of categories via OWL and using reasoners to take instances of contracts and sort them into various categories. Or XML trade messages can be translated into RDF with OWL reasoners then clumping all derivatives that have a contract pattern into a single class; calculations can be performed that could lead to discoveries – for example, that some party has taken a position for which it doesn’t have the assets on hand to make the required payouts, should circumstances require that.

As Rhyne reminds, “no one could figure out AIG had accumulated so much risk in terms of credit default swaps and support agreements. So regulators want a way to identify the counterparties to financial transactions.” By the way, he notes, they’re still working on unraveling all of AIG. And there are plenty of circumstances where owning parents of wholly- or partially-owned subsidiaries can be buried deep within tens or even hundreds of thousands of legal documents, as was done in the mortgage-backed securities industry. Currently, it can take years for courts to figure out actual ownership when legal problems come to the fore. The ontology’s notions of ownership and liability also align with the financial sector’s concern about ensuring information about what they own doesn’t leak to their competitors, by using unique but opaque identifiers for entities called Legal Entity Identifier (LEI).

In-depth sussing out of trades can be as important to the financial institutions as it is to regulators. Large financial companies have multiple trading desks – mortgage-backed securities, for instance, and currency swaps – and record-keeping problems around accumulated risk positions. Typically they have insight into risks for each particular desk, but not their overall positions, and rogue trades can complicate matters further by dressing up the books such that internal bank auditors have a hard time catching problems, like the failure for a trader to close a compensating position to make up for a risky position he’s taken. “So a financial ontology that can classify things will let you uncover situations where you have something that looks like a hedge but in fact, one side of the hedge has never been confirmed,” Rhyne says.

“In other words,” as Kendall puts it, “this stuff is really, really complicated.” And semantic technology alone will not be the soup-to-nuts solution. “It’s not just OWL reasoning but also sophisticated analytics and rules engines that are needed to really unravel all the connections to these things, so you can calculate what the risk is, who owns what,” she says. “Calculations are not the strength of OWL-based reasoners so you need other engines for that. But those engines are not good at understanding and following relationships across everything, and OWL reasoning lets you follow the threads. … So you need an ontology that’s mapped very cleanly to the math that the rules engines need, so that you can then truly ‘follow the money.’”

FIBO On the Way

FIBO is a starting point for all this, and FIBO’s starting point – the piece that will be formalized first by the OMG, which is now in draft – relates to generally describing business entities including legal entities. “This piece lets you make those connections across counter-parties even if you don’t have all the depth yet about the financial instruments themselves,” Kendall says. This will be available sometime later this year as a full standard from OMG. “We think that piece alone is broadly applicable. Lots of ontologies describe businesses – their address, their EDGAR filings. But there is no real standard ontology for that.”

FIBO, whose primary author is Mike Bennett, head of semantics and standards at the EDM Council and curator of the project, is going to be a modular standard, and that’s one area of Kendall’s role in the project is focused. So, this first piece of the ontology will service not only the Wall Street sector, but can be picked up for use by any business that needs to describe itself to customers or partners or suppliers, albeit from the financial services perspective. “We want to modularize FIBO so there are general-purpose pieces that people could reuse without having to commit to the whole thing,” she says. “Descriptions of business from the banking perspective is a fairly common requirement, so we don’t think that will make it overly onerous.”

She’s also honing the conformance angle, so that whether people want to use the modular ontology for lighter-weight business descriptions or deep financial bits and pieces, they will be able to state what parts of the ontology their applications conform to. “Conformance with a standard is a really big deal,” she notes. “But a lot of the terminology that is the basis of the ontology has already been really well-vetted.”

Interconnections With SBVR

At yesterday’s Demystifying Financial Services Semantics conference the audience also heard about OMG standard SBVR, Semantics of Business Vocabulary and Business Rules and its role in the machine-readable, natural language representation of financial services’ business rules and concepts. The idea is that business users can understand and use the conceptual semantic information models created and managed by enterprise architects, and IT and data management staff, without having to themselves be IT experts. And with that, they’ll progress in getting the ambiguity out of documents, especially an issue around governance.

Said Donald Chapin, principal consultant, Business Semantics, the way to gauge the success of SBVR efforts “is that your content authors actually use it like they would use a dictionary for spell-checking in the creation and authoring of content.” FIBO and SBVR, he noted, are two meta-models that are semantically equivalent and a key thing is get FIBO content into the SBVR interchange format, which is a work in progress. Also in progress is a beta SBVR vocabulary for date and time concepts, said Mark H. Linehan, of IBM Research.

There also is FIBO work that’s just started around developing analyst and ontologist-level certification programs, said Bennett of the EDM Council at the event. “Certification and training, to say someone is a FIBO-certified practitioner, is an important part of the ecosystem of the new standard,” he said.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.